Apr 2022 – Dec 2022 · 8 mos

• As a Graduate Student Researcher under Dr. Mai H Nguyen, advised by Prof. Garrison Cottrell, I presented a poster on the effects of multimodal data in wildfire smoke detection at the "Tackling Climate Change with Machine Learning" workshop at NeurIPS 2022. Our team documented our work in a 4-page paper available at https://arxiv.org/abs/2212.14143. Additionally, we submitted and had accepted the full-length transcript of our research to the Remote Sensing Journal, Section: Environment Remote Sensing, under the special issue: "Detecting, Mapping, and Characterizing Wildfires Using Remote Sensing Data," with the ISSN 2072-4292.

Oct 2021 – Mar 2022 · 5 mos

• As a research assistant, I conducted tests to evaluate the effectiveness of several state-of-the-art deep learning-based computer vision models for early and accurate wildfire detection. I was responsible for implementing the batch validation API for PyTorch Lightning and converting the legacy model from Torch to ONNX for field deployment. Additionally, I developed Python and Bash scripts to merge frames at different frame rates, creating videos with image tiles and dimensions plotted on each frame.

Nov 2020 – Jun 2021 · 7 mos

• I led a study aimed at enhancing the detection system for orthopedic Total Hip Arthroplasty (THA) implants through deep learning techniques. Collaborating closely with medical experts from University College London, Luton and Dunstable University Hospital, and the University of Kentucky, I contributed a deep learning approach to detect hip implants. Our findings were presented through an e-poster at the 41st International Society of Orthopaedic Surgery and Traumatology (SICOT) World Congress, held in Budapest in 2021.

Sep 2020 – May 2021 · 8 mos · Chennai, Tamil Nadu, India

Jul 2019 – Jan 2020 · 6 mos · Chennai, Tamil Nadu, India

Oct 2018 – Jun 2019 · 8 mos · Chennai, Tamil Nadu, India

Jun 2019 – Jun 2019 · 0 mo · Navi Mumbai, India · On-site

• I leveraged the power of the cognitive services API by Microsoft Azure to train a model which classifies various kinds of fruits based on their image with an accuracy of 95.8% on the test set. This increased reliability of the company to port the API for other use cases

Dec 2018 – Jan 2019 · 1 mo · New Delhi Area, India

• During my tenure at Studyopedia.com, I served as a technical subject matter expert, primarily focusing on crafting comprehensive interview questions pertaining to Java 8, Java 9, and Java SE. With a meticulous approach to content creation, I delved deep into the intricacies of these programming languages, ensuring that the questions were not only relevant but also challenging enough to gauge candidates' proficiency accurately. My dedication to delivering high-quality, informative content significantly contributed to enhancing the learning experience of over 100,000 users who visited the website seeking guidance and preparation for Java-related interviews.